In order to meet the increasing demand for wireless data traffic since the commercialization of 4G communication systems, the development focus is on the 5th Generation (5G) or pre-5G communication system. For this reason, the 5G or pre-5G communication system is called a beyond 4G network communication system or post Long Term Evolution (LTE) system. In order to accomplish high data rates, consideration is being given to implementing the 5G communication system on the millimeter Wave (mm Wave) band (e.g., 60 GHz band). In order to mitigate propagation loss and increase propagation distance, the 5G communication system is likely to accommodate various techniques such as beamforming, massive MIMO, Full Dimensional MIMO (FD-MIMO), array antenna, analog beamforming, and large scale antenna. Also, for throughput enhancement of the 5G communication system, research is being conducted on various techniques such as small cell, advanced small cell, cloud radio access network (cloud RAN), ultra-dense network, Device to Device (D2D) Communication, wireless backhaul, moving network, cooperative communication, Coordinated Multi-Points (CoMP), and interference cancellation. Furthermore, the ongoing research includes the use of Hybrid FSK and QAM modulation and Sliding Window Superposition Coding (SWSC) as Advanced Coding Modulation (ACM), Filter Bank Multi Carrier (FBMC), Non-Orthogonal Multiple Access (NOMA), and Sparse Code Multiple Access (SCMA).
Meanwhile, the Internet is evolving from a human-centric communication network in which information is generated and consumed by humans to the Internet of Things (IoT) in which distributed things or components exchange and process information. The combination of the cloud server-based Big data processing technology and the IoT begets Internet of Everything technology. In order to secure the sensing technology, wired/wireless communication and network infrastructure, service interface technology, and security technology required for implementing the IoT, recent research has focused on sensor network, Machine to Machine (M2M), and Machine Type Communication (MTC) technologies. In the IoT environment, it is possible to provide an intelligent Internet Technology that is capable of collecting and analyzing data generated from connected things to create new values for human life. The IoT can be applied to various fields such as smart home, smart building, smart city, smart car or connected car, smart grid, health care, smart appliance, and smart medical service through legacy Information Technology (IT) and convergence of various industries.
Thus there are various attempts to apply the IoT to the 5G communication system. For example, sensor network, Machine to Machine (M2M), and Machine Type Communication (MTC) technologies are implemented by means of the 5G communication technologies such as beamforming, MIMO, and array antenna. The application of the aforementioned cloud RAN as a big data processing technology is an example of convergence between the 5G and IoT technologies.
The mobile communication system has evolved to a high-speed, high-quality wireless packet data communication system capable of providing data and multimedia services beyond the early voice-oriented services. Recently, various mobile communication standards, such as High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), and LTE-Advanced (LTE-A) defined in 3rd Generation Partnership Project (3GPP); High Rate Packet Data (HRPD) defined in 3rd Generation Partnership Project-2 (3GPP2); and 802.16 defined in IEEE, have been developed to support the high-speed, high-quality wireless packet data communication services. In particular, LTE is a communication standard developed to support high speed packet data transmission and to maximize the throughput of the radio communication system with various radio access technologies. LTE-A is the evolved version of LTE for improving data transmission capability.
Typically, LTE base stations and terminals are based on 3GPP Release 8 or 9 while LTE-A base stations and terminals are based on 3GPP Release 10. The 3GPP standard organization is preparing for the next release for more improved performance beyond LTE-A.
The existing 3rd and 4th generation wireless packet data communication systems (such as HSDPA, HSUPA, HRPD, and LTE/LTE-A) adopt Adaptive Modulation and Coding (AMC) and channel sensitive scheduling techniques to improve the transmission efficiency.
AMC allows the transmitter to adjust the data amount to be transmitted in adaptation to the channel condition. That is, the transmitter is capable of decreasing the data transmission amount for poor channel condition so as to maintain the received signal error probability at a certain level or increasing the data transmission amount for good channel condition so as to transmit large amount of information efficiently while maintaining the received signal error probability at an intended level.
Channel sensitive scheduling allows the transmitter to serve the user having good channel conditions selectively among a plurality of users, thereby increasing the system capacity in comparison with serving a single user by allocating a channel fixedly. This increase in system capacity is referred to as multi-user diversity gain
Both the AMC and channel sensitive scheduling are methods of adopting the best modulation and coding scheme at the most efficient time based on the partial channel state information feedback from the receiver.
In case of using AMC along with the Multiple Input Multiple Output (MIMO) transmission scheme, it may be necessary to take into consideration a number of spatial layers and ranks for transmitting signals. In this case, the transmitter determines the optimal data rate in consideration of the number of layers for use in MIMO transmission as well as coding rate and modulation scheme.
A MIMO scheme for transmitting radio signals with a plurality of transmit antennas is categorized into one of two main categories: Single User MIMO (SU-MIMO) for transmitting signals to one terminal and Multi User MIMO (MU-MIMO) for transmitting signals to multiple users. In the case of the SU-MIMO scheme, multiple transmit antennas are used for transmitting multiple radio signal streams to a single user over a plurality of spatial layers. At this time, the receiver has to have a plurality of receive antennas to support multiple spatial layers. In the case of MU-MIMO, multiple transmit antennas are used for transmitting multiple radio signal streams to multiple users over a plurality spatial layers.
In comparison with SU-MIMO, MU-MIMO is advantageous in that there is no need for the receiver to have multiple receive antennas. However, the radio signals transmitted to different receivers using the same frequency and time resources are likely to interfere with each other.
Recently, much research has been conducted to replace the Code Division Multiple Access (CDMA) used in the legacy 2nd and 3rd mobile communication systems with Orthogonal Frequency Division Multiple Access (OFDMA) for the next generation mobile communication system. The 3GPP and 3GPP2 are in the middle of standardization of the OFDMA-based evolved system. OFDMA is expected to provide superior system throughput compared with CDMA. One of the main factors that allows OFDMA to increase system throughput is the frequency domain scheduling capability. Like the channel sensitive scheduling scheme which is capable of achieving the capacity gain through scheduling in adaptation to the time-varying channel condition, it is possible to achieve more capacity gain using the frequency-varying channel characteristic.
FIG. 1 is a diagram illustrating a time-frequency resource grid utilized in an LTE/LTE-A system.
In FIG. 1, the radio resources for transmission from the evolved Node B (eNB) to a User Equipment (UE) are divided into Resource Blocks (RBs) in the frequency domain and subframes in the time domain. In the LTE/LTE-A system, an RB consists of 12 consecutive carriers and has a bandwidth of 180 kHz. Meanwhile, a subframe consists of 14 OFDM symbols and spans 1 msec. The LTE/LTE-A system allocates resources for scheduling in unit of subframe in the time domain and in unit of RB in the frequency domain.
In the LTE/LTE-A system, scheduling is performed by allocating resources in unit of time domain subframe and frequency domain RB.
FIG. 2 is a diagram illustrating radio resources corresponding to a subframe and an RB as a smallest resource allocation unit for downlink scheduling in an LTE/LTE-A system.
The radio resources depicted in FIG. 2 are of one subframe in the time domain and one RB in the frequency domain. The radio resources consist of 12 subcarriers in the frequency domain and 14 OFDM symbols in the time domain, i.e. 168 unique frequency-time positions. In LTE/LTE-A, each frequency-time position is referred to as a Resource Element (RE). One subframe consists of two slots, and each slot consists of 7 OFDM symbols.
The resources can be structured for transmitting multiple different types of signals as shown in FIG. 2. Examples of the different types of signals may include a Cell Specific Frequency Signal (CRS) 200, a Demodulation Reference Signal (DMRS) 202, a Physical Downlink Shared Channel (PDSCH) 204, a Channel Status Information Reference Signal (CSI-RS) 206, and other control channel signals 208.
The CRS is a reference signal broadcast for all UEs within a cell (i.e., cell-specific signal).
The DMRS is a reference signal transmitted to a specific UE (i.e., UE-specific signal).
The PDSCH is a data channel for downlink transmission. The PDSCH designed for an eNB to transmit data traffic to a UE may be used for transmitting reference signals at the REs that are not used in the data region 210 of the radio resources.
The CSI-RS is a reference signal transmitted for the UEs located within a cell for use in channel state measurement. It may be possible for multiple CSI-RSs to be transmitted within a cell.
Other control channel signals 208 may be the signals carrying control information for use by a UE in receiving PDSCH or ACK/NACK concerning Hybrid Automatic Repeat Request (HARQ) that is transmitted in correspondence to uplink data transmission. Examples of the control channel signals may include Physical Hybrid-ARQ Indicator Channel (PHICH), Physical Control Format Indicator Channel (PCFICH), and Physical Downlink Control Channel (PDCCH).
In addition to the above signals, muting may be configured in order for the UEs within the corresponding cells to receive the CSI-RSs transmitted by different eNBs in the LTE-A system. The muting can be mapped to the positions designated for CSI-RS, and in general the UE receives the traffic signal skipping the corresponding radio resource. In the LTE-A system, muting is referred to as zero power CSI-RS (ZP CSI-RS). Muting by nature is mapped to the CSI-RS position without transmit power allocation.
In FIG. 2, the CSI-RS can be transmitted at some of the positions marked by A, B, C, D, E, F, G, H, I, and J according to the number of antennas transmitting CSI-RS. Also, the zero power CSI-RS (muting) can be mapped to some of the positions A, B, C, D, E, F, G, H, I, and J.
The CSI-RS can be mapped to 2, 4, or 8 REs according to the number of the antenna ports for transmission. For two antenna ports, half of a specific pattern is used for CSI-RS transmission; for four antenna ports, all of the specific pattern is used for CSI-RS transmission; and for eight antenna ports, two patterns are used for CSI-RS transmission.
Meanwhile, muting is always performed by pattern. That is, although the muting may be applied to plural patterns, if the muting positions mismatch CSI-RS positions, muting cannot be applied to one pattern partially. However, if the CSI-RS positions match the zero power CSI-RS (muting) positions, the muting can be applied to a part of one pattern.
In a cellular system, the reference signal has to be transmitted for downlink channel state measurement. In the case of the 3GPP LTE-A system, the UE measures the channel state with the eNB using the CSI-RS transmitted by the eNB.
The channel state is measured in consideration of a few factors including downlink interference. The downlink interference includes the interference caused by the antennas of neighbor eNBs and thermal noise, which are important in determining the downlink channel condition. For example, in the case that an eNB with one transmit antenna transmits a reference signal to the UE with one receive antenna, the UE has to determine the energy per symbol that can be received in downlink and any interference amount that may be received for the duration of receiving the corresponding symbol to acquire the Signal to Noise plus Interference Ratio (SNIR). The SNIR is the value obtained by dividing the received signal power by interference and noise signal strength. Typically, the signal reception performance and data rate is in proportion to the SNIR. If the SNIR is determined, the UE reports to the eNB the determined SNIR, a value corresponding to the SNIR, or a maximum data rate corresponding to the SNIR such that the eNB determines the downlink data rate for the UE.
In a mobile communication system, it is typical that a base station is located at the center of each cell and equipped with one or more antennas for supporting mobile communication of mobile stations. A mobile communication system in which the antennas belonging to one cell are arranged at the same position is referred to as Centralized Antenna System (CAS). Meanwhile, a mobile communication system in which the antennas belonging to one cell are distributed within the cell is referred to as Distributed Antenna System (DAS).
FIG. 3 is a diagram illustrating an antenna arrangement of a legacy distributed antenna system.
FIG. 3 exemplifies a distributed antenna system comprised of 2 cells 300 and 310.
For example, a cell 300 includes one high power transmit antenna 320 and four low power transmit antennas 340. The high power transmit antenna 320 is configured to provide at least minimum service within the coverage area of the cell while the low power transmit antennas 340 are configured to provide the UEs with the service at a high data rate within a restricted area of the cell. All of the high and low power transmit antennas 320 and 340 are connected to a central controller as denoted by reference number 330 and operating according to scheduling and radio resource allocation of the central controller. In the distributed antenna system, one or more antennas may be arranged at each of the geographically distributed antenna positions. In the distributed antenna system, the antenna(s) deployed at the same position is referred to as Remote Radio Head (RRH)
In the distributed antenna system as exemplified in FIG. 3, the UE receives the signal radiated by one geometrically distributed antenna group, but it regards the signals radiated by other antenna groups as interference.
FIG. 4 is a diagram illustrating an exemplary interference situation where multiple antenna groups transmit signals to different UEs in a distributed antenna system.
In FIG. 4, the solid arrows represent desired signals, and the dotted arrows represent interference. The User Equipment (UE) 1 400 is receiving traffic signals from the antenna group 410. Meanwhile, the UE2 420 is receiving signals from the antenna group 430, the UE3 440 from the antenna group 450, and the UE4 460 from the antenna group 470. While receiving the traffic signals radiated by the antenna group 410, the UE1 400 undergoes interference caused by the traffic signals radiated from other antenna groups to other UEs. That is, the signals transmitted through the antenna groups 430, 450, and 470 cause interference to the UE1 400.
In the distributed antenna system, the interference caused by other antenna groups may be categorized into two categories: inter-cell interference and intra-cell interference. The inter-cell interference is a type of interference occurring between the antenna groups belonging to different cells, and the intra-cell interference is a type of interference occurring between the antenna groups belonging to one cell.
In FIG. 4, the UE 1 undergoes intra-cell interference from the antenna group 430 of the same cell (i.e., cell 1) and inter-cell interference from the antenna groups 450 and 470 of a neighbor cell (i.e., cell 2. The inter-cell interference and the intra-call interference may have a negative effect on the data channel reception of the UE.
Typically, the signal received by a UE consists of the desired signal, noise, and interference. The received signal may be expressed by equation (1).r=s+noise+interference  (1)
Here, “r” denotes the received signal, “s” denotes the transmitted signal, “noise” denotes noise with Gaussian distribution, and “interference” denotes interferer signals occurring in radio communication.
The interference may be caused by the signal radiated from a neighboring transmission point (e.g., neighboring cell) or the identical transmission point (e.g., serving cell). The neighboring transmission point interference is observed when a signal transmitted from a neighboring cell or radiated from a neighboring antenna of the distributed system affects the desired signal. The identical transmission point interference is observed when the signals destined for different users interfere with each other in the case of MU-MIMO transmission in which one transmission point uses multiple transmit antennas.
The SNIR varies according to the amount of interference, which affects the reception performance. In the cellular mobile communication system, the ability to control interference (as one of the main factors contributing to system performance degradation) efficiently determines the system performance.
In LTE/LTE-A, consideration is being given to introducing various standardized techniques to support a Network Assisted Interference Cancellation and Suppression (NAICS) technology in order to improve reception performance in an interference situation. The NAICS technology is characterized in that an eNB transmits to the corresponding UE the interferer signal information through a network in order for the UE to recover the transmitted signal in consideration of the characteristics of the interferer signal. If the UE is aware of the modulation scheme applied to the interferer signal, it may cancel the interferer signal or recover the transmitted signal in consideration of the interferer signal to improve the reception performance.
In a wireless communication system, an error correction code is used to correct errors occurring during communication. In an LTE/LTE-A system, a convolution code and a turbo code are used as error correction codes. In order to improve the decoding performance of the error correction code, the receiver uses soft decision making rather than hard decision making in demodulating the symbol modulated with QPSK, 16QAM, 64QAM, or the like. If “+1” or “−1” is transmitted by the transmitter, the receiver, which makes a hard decision, selects one of “+1” and “−1” and outputs the selection result. In contrast, the receiver, which makes a soft decision, outputs the information indicating the selection made between “+1” and “−1” and the reliability of decision making. The reliability information can be used for improving the decoding performance in the decoding process. Typically, the receiver that makes soft decision making uses a Log Likelihood Ratio (LLR) for calculating the output value. In the case that a BPSK modulation scheme having the output value of “+1” or “−1” is applied to the transmission signal, the LLR is defined as follows.
                    LLR        =                  log          ⁢                                    f              ⁡                              (                                                      r                    ❘                    s                                    =                                      +                    1                                                  )                                                    f              ⁡                              (                                                      r                    ❘                    s                                    =                                      -                    1                                                  )                                                                        (        2        )            
In equation (2), “r” denotes the reception signal, and “s” denotes the transmission signal. The conditional probability density function ƒ(r|s=+1) is of the reception signal under the assumption that “+1” is transmitted as the transmission signal. In the QPSK, 16QAM, and 64QAM schemes, the LLR can be expressed in a similar way. The conditional probability density function has Gaussian distribution in the situation where interference exists.
FIG. 5 is a diagram illustrating an exemplary conditional probability density function graph.
In FIG. 5, the first curve 500 denotes a conditional probability density function ƒ(r|s=−1), and the second curve 510 denotes another conventional probability density function ƒ(r|s=+1). In the case that the received signal has the value corresponding to the second curve 510, the receiver calculates LLR with log(f2/f1). The conditional probability density functions of FIG. 5 correspond to the case where the noise and interference have Gaussian distribution.
In the LTE/LTE-A mobile communication system, an eNB transmits a few dozen or more bits of information to the UE through single Physical Downlink Shared Channel (PDSCH) transmission. The eNB encodes the information to be transmitted to the UE and modulates the encoded information with a modulation scheme such as QPSK, 16QAM, and 64QAM. Accordingly, if the PDSCH is received, the UE may generate LLRs of a few dozen or more coded symbols to the decoder.
Typically, noise has Gaussian distribution, but interference may not have Gaussian distribution depending on the situation. The reason why interference does not have Gaussian distribution is that the interference is a radio signal in view of other receivers. That is, since the “interference” of equation (1) denotes radio signals transmitted to other receivers, at least one of the BPSK, QPSK, 16QAM, and 64QAM schemes is applied thereto. In an exemplary case that the interferer signal is modulated with BPSK, the interference has a probability distribution of “+k” or “−k” at the same probability. Here, “k” is a value determined by the signal strength attenuation effect on the radio channel.
FIG. 6 is a diagram illustrating an exemplary conditional probability density function graph under the assumption that both the desired signal and interferer signal are modulated with BPSK. In FIG. 6, it is assumed that the noise has Gaussian distribution.
It can be observed that the conditional probability density function of FIG. 6 differs from that of FIG. 5. In FIG. 6, the first curve 620 denotes the conditional probability density function ƒ(r|s=−1), and the second curve 630 denotes the conditional probability density function ƒ(r|s=+1). The size of the distribution distance 610 is determined according to the signal strength of the interferer signal and depends on the influence of the radio channel. In the case that the received signal value corresponds to the first curve 600 of FIG. 6 with the conditional probability density function, the receiver calculates LLR with log(f4/f3). This value differs from the LLR value in the case of FIG. 5 because of the difference in conditional probability density function. That is, the LLR obtained in consideration of the modulation scheme of the interferer signal differs from the LLR obtained under the assumption of Gaussian distribution.
FIG. 7 is a graph illustrating an exemplary conditional probability density function under the assumption that the interferer signal is modulated with 16QAM while the desired signal is modulated with BPSK.
In FIG. 7, the first curve 700 denotes the conditional probability density function ƒ(r|s=−1), and the second curve 710 denotes the conditional probability density function ƒ(r|s=+1). FIG. 7 shows that the conditional probability density function may be modified when the modulation scheme of the interference differs from that of the desired signal. The desired signal is modulated with BPSK in both the cases of FIGS. 6 and 7, while the interference is modulated with BPSK in FIG. 6 and 16QAM in FIG. 7. That is, although the desired signal is modulated with the same modulation scheme, the conditional probability density function varies according to the modulation scheme of the interferer signal, resulting in different LLRs.
As described with reference to FIGS. 5, 6, and 7, LLR may vary according to the interference that the receiver assumes for LLA calculation. In order to optimize the reception performance, it is necessary to calculate LLR using the conditional probability density function reflecting the statistical characteristic of the real interference or after canceling interference in advance. That is, if the interference is modulated in BPSK, the receiver has to calculate LLR under the assumption that the interferer signal is modulated with BPSK. In the state that the interference has been modulated with BPSK, if the receiver assumes Gaussian distribution or 16QAM, it fails to obtain the optimal LLR, resulting in degradation of reception performance.